Lower urinary tract dysfunction (LUTD) is common, and its incidence and prevalence are increasing as the population ages. Symptoms of LUTD are costly to diagnose and treat; existing therapeutic interventions are neither highly effective nor durable and have side effects of their own. Not surprisingly, unsatisfactory outcomes from the patient perspective are common. Collectively, these challenges represent important targets of research that are consistent with the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Previous efforts to improve patient reported outcomes (PRO) in this area have relied on incomplete symptom measurement scales and have been hampered by imperfect understanding of the origin and natural history of LUTD. Comprehensive re-evaluation of the symptom universe of LUTD and more precise characterization of patients with symptoms of LUTD (phenotyping) may provide significant new insights, leading ultimately to improved patient lives. The NIDDK-sponsored Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) includes a Data Coordinating Center (DCC) to serve as the project and data management nexus for the network. The purpose of this project is to establish the DCC, which will form a collaborative relationship with NIDDK and several clinical sites that will enroll patiets with symptoms of LUTD into carefully designed studies. There are two specific aims. The first is to develop the infrastructure to support the entire research network. This will include expertise and experience in the areas of research coordination, communications and logistics, study design, database design and centralized data management, study monitoring, quality assurance, biosample handling and tracking, and recognized excellence in the application of a wide range of statistically rigorous analysis methods. The second specific aim embodies the scientific goals of the project, including: 1) development of PRO measurement tools to quantitate symptoms of LUTD in women and men using state-of-the-art scale development and statistical analysis methods; 2) development of deep phenotyping of patient cohorts with relevant LUTD symptoms using cluster analysis, classification and regression trees, and other data mining methods; and 3) identification of biomarkers related to symptom initiation, exacerbation, mitigation, remission, and progression.